Hydraulic redistribution (HR) on the field scale: Novel approaches for monitoring and assessing the implications of HR for the resilience of forest ecosystems

面对全球变化，全球生态系统正面临严峻挑战。在欧洲，由于干旱的直接和间接影响，最近的干旱造成了毁灭性的森林死亡。水力再分配(HR)--水分通过植物根系在不同土壤部分之间的被动移动--是一个可能对生态系统适应干旱的能力产生影响的过程。水力提升(HL)是最常见的HR形式，由于湿润的深层土壤和干燥的表层土壤之间的水势梯度，水分在夜间被动地运移到干燥的土壤区域，可能为植物提供重要的水源。二十多年的人力资源研究表明，这是一个对生态系统具有全球重要性的过程。尽管证明了这一全球重要性，但在比树尺度和证据支持的建模方法更大的空间尺度上，关于人力资源的一致数据集极其稀少。因此，我们目前无法准确预测，在预测的气候变化下，人力资源是否将成为森林和其他生态系统恢复能力的重要组成部分。在这个项目中，我们将首次收集空间分布(野外尺度，高达1公顷)和时间(分日至周)的高分辨率HR数据集。利用这个数据集，我们的目的是调查人力资源在实地范围内的相关性，评估人力资源的异质性，并确定谁是人力资源的受益者。为了实现这一目标，我们将建立一个专门为识别、监测和量化人力资源而设计的监测计划。这一设置将基于现场测量水稳定同位素的方法与特定的生态水文监测技术和混合阔叶林中的同位素标记实验相结合。该数据集随后将被用于对土壤-植被-大气传输(SVAT)模型Musica进行参数化和校准，并使用端元混合分析(EMMA)来确定田间尺度上HR对蒸腾的贡献。然后，将使用校准后的模型来预测HR对于所调查的阔叶林生态系统在气候变化下的弹性的重要性。该项目的结果将有助于理解HR在目前和预测的未来气候条件下对森林生态系统的作用。它将有助于评估人力资源在干旱和极端条件下的潜在缓冲作用，这有助于为森林管理提供信息，以发展未来有弹性的森林生态系统。此外，拟议的方法框架有可能成为在更大空间尺度上评估人力资源的标准程序。

In the face of global change, ecosystems world-wide are experiencing severe challenges. In Europe, the recent drought has caused devastating forest dieback due to direct and indirect effects of drought. Hydraulic redistribution (HR) - the passive movement of water between different soil parts via plant root systems - is a process that can potentially have an impact on the capacity of ecosystems to adapt to droughts. Hydraulic lift (HL) is the most common form of HR, where due to water potential gradients between wetter deep soil and dry surface soil water passively moves to the dry soil region during the night, potentially providing an important water source to be used by plants. More than two decades of research on HR have demonstrated that it is a process with a global importance for ecosystems. Despite this proven global importance, consistent datasets on HR on spatial scales larger than the tree-scale and evidence-backed modeling approaches are extremely scarce. As a consequence, we are currently not able to predict accurately if HR will be an important component for the resilience of forests and other ecosystems under predicted climatic changes. In this project, we will – for the first time – collect a spatially distributed (field-scale, up to 1 ha) and temporally (sub-daily to weekly) high resolution dataset on HR. With this dataset we aim at investigating the relevance of HR on the field scale, assess the heterogeneity of HR and identify who are the beneficiaries of HR. In order to achieve this, we will establish a monitoring scheme specifically designed to identify, monitor and quantify HR. This setup combines methods based on the in situ measurement of water stable isotopes with specific ecohydrological monitoring techniques and isotopic labeling experiments in a mixed broad-leaf forest. The dataset will subsequently be used to parametrize and calibrate the isotope-enabled soil-vegetation-atmosphere transfer (SVAT) model MuSICA and determine the contribution of HR to transpiration on the field scale using end-member mixing analysis (EMMA). The calibrated model will then be used in order to predict the importance of HR for the resilience of the investigated mixed broad-leaf forest ecosystem under climatic changes. The results of this project will help to understand the role of HR for forest ecosystems now and under predicted future climatic conditions. It will contribute to assess the potential buffering effect of HR during droughts and extreme conditions, which can help to inform forest management in terms of developing resilient forest ecosystems of the future. Furthermore, the proposed methodological framework could potentially become a standard procedure for assessing HR on larger spatial scales.